Atomic Structure Optimization with Machine-Learning Enabled Interpolation between Chemical Elements

Sami Kaappa, Casper Larsen, Karsten Wedel Jacobsen*

*Corresponding author for this work

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We introduce a computational method for global optimization of structure and ordering in atomic systems. The method relies on interpolation between chemical elements, which is incorporated in a machine-learning structural fingerprint. The method is based on Bayesian optimization with Gaussian processes and is applied to the global optimization of Au-Cu bulk systems, Cu-Ni surfaces with CO adsorption, and Cu-Ni clusters. The method consistently identifies low-energy structures, which are likely to be the global minima of the energy. For the investigated systems with 23-66 atoms, the number of required energy and force calculations is in the range 3-75.

Original languageEnglish
Article number166001
JournalPhysical Review Letters
Issue number16
Number of pages6
Publication statusPublished - 2021

Bibliographical note

Funding Information:
We acknowledge support from the VILLUM Center for Science of Sustainable Fuels and Chemicals, which is funded by the VILLUM Fonden Research Grant (No. 9455).


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